Computing Paradigms for Smart Healthcare

Smart healthcare has gradually gained its popularity because of the development of information technologies and computing paradigms such as internet of things (loT), big data, Cloud computing and artificial intelligence. These technologies transform the conventional medical system in to smarter one by making healthcare more convenient, efficient, accurate, and more customized. Smart healthcare will lead to a revolutionized healthcare system that enables the participation of all people for the early prediction and prevention of diseases so that preemptive and pro-active treatment can be delivered. The aim of this edited book is to publish the latest research advancements in the convergence of automation technology, artificial intelligence, biomedical engineering and health informatics. This will help the readers to grasp the extensive point of view and the essence of the recent advances in this field. This book solicits contributions which include theory, case studies and computing paradigms pertaining to the healthcare applications. The prospective audience would be researchers, professionals, practitioners, and students from academia and industry who work in this field. We hope the chapters presented will inspire future research both from theoretical and practical viewpoints to spur further advances in the field. The entire book is the contribution of interdisciplinary expertise available in the esteemed Institution PSG College of Technology, an ISO 9001:2015 certified Government aided Institution, belonging to Department of Information Technology, Computer Science and Engineering, Electronics and Communication Engineering, Biomedical Engineering and Biotechnology. A brief introduction about each chapter is as follows. Chapter 1 focuses on health informatics which provides an overview of the various types of data originating from the medical information, Chapter 2 objective is to provide a 'smart connected environment' which includes storing, processing and exchange information seamlessly using technologies. Chapter 3 deals with an intelligent healthcare system for automatic diagnosis of diseases based on IOT enabled cloud computing framework and deep learning Chapter 4 discuss about basic concepts of digital twin technology and implementation of digital twin in various health care domains. Chapter 5 proposes a graph-based framework for classification, feature selection method which uses the existing notion, histograms for extracting isotonic features from a data set. Chapter 6 explains the significance of convolution neural network in medical image analysis. Chapter 7 summarizes recent advances in AI tools applied in cancer diagnosis and research for disease prediction and biomarker discovery. Chapter 8 explores DNA microarray data followed by the implementation of machine learning algorithms to obtain the highly predictive genes for classification. Chapter 9 uses various data structures such as hash tables and prefix-based search trees to efficiently query the EHR data present in the Blockchain. Chapter 10 focuses on agreeing upon a common symmetric cryptographic key generated from the ECG signal collected at different locations of a patient using linear prediction and error control coding techniques. We are grateful to the authors and reviewers for their excellent contributions for making this book possible.

Smart healthcare has gradually gained its popularity because of the development of information technologies and computing paradigms such as internet of things (loT), big data, Cloud computing and artificial intelligence. These technologies transform the conventional medical system in to smarter one by making healthcare more convenient, efficient, accurate, and more customized. Smart healthcare will lead to a revolutionized healthcare system that enables the participation of all people for the early prediction and prevention of diseases so that preemptive and pro-active treatment can be delivered. The aim of this edited book is to publish the latest research advancements in the convergence of automation technology, artificial intelligence, biomedical engineering and health informatics. This will help the readers to grasp the extensive point of view and the essence of the recent advances in this field. This book solicits contributions which include theory, case studies and computing paradigms pertaining to the healthcare applications. The prospective audience would be researchers, professionals, practitioners, and students from academia and industry who work in this field. We hope the chapters presented will inspire future research both from theoretical and practical viewpoints to spur further advances in the field. The entire book is the contribution of interdisciplinary expertise available in the esteemed Institution PSG College of Technology, an ISO 9001:2015 certified Government aided Institution, belonging to Department of Information Technology, Computer Science and Engineering, Electronics and Communication Engineering, Biomedical Engineering and Biotechnology. A brief introduction about each chapter is as follows. Chapter 1 focuses on health informatics which provides an overview of the various types of data originating from the medical information, Chapter 2 objective is to provide a 'smart connected environment' which includes storing, processing and exchange information seamlessly using technologies. Chapter 3 deals with an intelligent healthcare system for automatic diagnosis of diseases based on IOT enabled cloud computing framework and deep learning Chapter 4 discuss about basic concepts of digital twin technology and implementation of digital twin in various health care domains. Chapter 5 proposes a graph-based framework for classification, feature selection method which uses the existing notion, histograms for extracting isotonic features from a data set. Chapter 6 explains the significance of convolution neural network in medical image analysis. Chapter 7 summarizes recent advances in AI tools applied in cancer diagnosis and research for disease prediction and biomarker discovery. Chapter 8 explores DNA microarray data followed by the implementation of machine learning algorithms to obtain the highly predictive genes for classification. Chapter 9 uses various data structures such as hash tables and prefix-based search trees to efficiently query the EHR data present in the Blockchain. Chapter 10 focuses on agreeing upon a common symmetric cryptographic key generated from the ECG signal collected at different locations of a patient using linear prediction and error control coding techniques. We are grateful to the authors and reviewers for their excellent contributions for making this book possible.